Rather than directly regulating AI algorithms or models themselves, this approach uses "controlling who can access how much training compute" as a policy lever — since training frontier AI models is inseparable from large quantities of specific-spec chips, and the physical supply chain for those chips is comparatively easy to track and control, making compute itself one of the most mainstream AI governance tools in use today.